Variables selection using \(\mathcal{L}_0\) penalty

DOI10.1016/j.csda.2023.107860OpenAlexW4387046694MaRDI QIDQ6071717

Tonglin Zhang

Publication date: 28 November 2023

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2023.107860

zbMATH Keywords

consistencyhigh-dimensional datageneralized linear modelspenalized maximum likelihoodgeneralized information criterionmodel size

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Sure independence screening in generalized linear models with NP-dimensionality
• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Consistent tuning parameter selection in high dimensional sparse linear regression
• A functional central limit theorem for weakly dependent sequences of random variables
• Relaxed Lasso
• Functional central limit theorems for triangular arrays of function-indexed processes under uniformly integrable entropy conditions
• A comparison of programming languages in macroeconomics
• Efficient regularized regression with \(L_0\) penalty for variable selection and network construction
• Best subset, forward stepwise or Lasso? Analysis and recommendations based on extensive comparisons
• Calibrating nonconvex penalized regression in ultra-high dimension
• Forward Regression for Ultra-High Dimensional Variable Screening
• Some Useful Functions for Functional Limit Theorems
• Computational Complexity of Discrete Optimization Problems
• Asymptotic Statistics
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• An Introduction to Functional Central Limit Theorems for Dependent Stochastic Processes
• A polynomial algorithm for best-subset selection problem
• Regularization Parameter Selections via Generalized Information Criterion
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• The Equivalence of Functional Central Limit Theorems for Counting Processes and Associated Partial Sums
• Tuning Parameter Selection in High Dimensional Penalized Likelihood

This page was built for publication: Variables selection using \(\mathcal{L}_0\) penalty